Neural Programming by Example

Title: Neural Programming by Example
Creator: Shu, Chengxun; Zhang, Hongyu
Relation: AAAI-17: Thirty-First AAAI Conference on Artificial Intelligence. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) (San Francisco, CA 04-09 February, 2017) p. 1539-1545
Relation: http://www.aaai.org/Conferences/AAAI/aaai17.php
Publisher: AAAI Press
Resource Type: conference paper
Date: 2017
Description: Programming by Example (PBE) targets at automatically inferring a computer program for accomplishing a certain task from sample input and output. In this paper, we propose a deep neural networks (DNN) based PBE model called Neural Programming by Example (NPBE), which can learn from input-output strings and induce programs that solve the string manipulation problems. Our NPBE model has four neural network based components: a string encoder, an input-output analyzer, a program generator, and a symbol selector. We demonstrate the effectiveness of NPBE by training it end-to end to solve some common string manipulation problems in spreadsheet systems. The results show that our model can induce string manipulation programs effectively. Our work is one step towards teaching DNN to generate computer programs.
Subject: NPBE; neural networks; DNN; neural enquirer
Identifier: http://hdl.handle.net/1959.13/1399616
Identifier: uon:34642
Language: eng
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